Ground AI workflows in telemetry.
Automation that ships, survives review, and never meters you per query. Datafabric gives your agents memory, context, and ground truth in one substrate, reached over MCP, so the workflows your team builds assert nothing they cannot evidence.
The stack your team builds on
Two products, one substrate.
Datafabric captures and retains full-fidelity telemetry inside your cloud, the memory and ground truth your agents stand on. SynthAI is the reasoning engine that runs over it, and your own agents reason over the same fabric through the same interface.
The grounding loop
Agents reason from ground truth.
An agent with no link to your telemetry works from assumptions, and it will state them with confidence. Wire the same agent to the fabric over MCP and its outputs stop being claims and start carrying evidence. Toggle the two architectures and watch what changes.
- context layer
- data catalog
- query generation
- 3 related logins, 2 hostsdf://identity · 90d
- host seen in a prior campaigndf://endpoint · 2y
Consistency
Same question, same answer.
When agents reason over one shared record instead of their own scraped context, the same question returns the same answer, run to run and agent to agent. Your automation becomes reviewable, because there is a single ground truth to review it against.
One record
Every agent reads the same full-fidelity telemetry, so answers do not drift with whoever asked.
Replayable
A decision made today can be run again tomorrow against the same records, and it holds.
Reviewable
Because the inputs are fixed and retained, a human can audit exactly what the agent reasoned over.
Evidence
Every output carries its citations.
A grounded recommendation arrives stamped with the records behind it: the identifiers, the timestamps, and the retention window it was drawn from. Your team can trace any conclusion back to the telemetry, and so can the next agent in the chain.
- recorddf://identity/session
- window90 days, full fidelity
- captured2026-07-08 01:44 UTC
Interface
MCP is how agents connect.
Agents connect over Model Context Protocol and reason over the fabric directly, with no glue code to write or maintain. A context layer and a data catalog mean an agent never needs to know what data exists where, or which query language to speak. The fabric generates the query, the context, and the answer, inside the cloud your team already owns.
Explore Datafabric →- 01
Connect
The agent speaks MCP. No connector code, no bespoke API layer.
- 02
Catalog
The context layer resolves what exists and where, so the agent does not have to.
- 03
Query
The fabric generates the query and returns the answer with its evidence attached.
Business outcomes
What the business gets back.
The products matter because of what they return: hours, budget certainty, and risk taken off the table. These are the outcomes your leadership will notice.
01
Automation that survives review
Evidence-attached decisions pass audit and governance gates, so agent projects actually ship to production instead of stalling in review.
Driven by · Datafabric · SynthAI
02
No parallel data platform to build
Agents reason over the fabric the business already runs. There is no second pipeline to build, sync, and secure before the first workflow delivers value.
Driven by · Datafabric
03
Leverage without headcount
Grounded agents take the first pass on triage, root cause, and routine checks, so the team automates work it would otherwise have to hire for.
Driven by · SynthAI · Datafabric
04
Costs that do not scale with usage
The substrate is sized once, so agent queries never meter you. Automation can run as often as it is useful, not as often as the budget allows.
Driven by · Datafabric
Feature map
What delivers what.
Each capability on this page comes from a specific product. Follow a row straight to the section that covers it in depth.
| Feature | Delivered by | Read more |
|---|---|---|
MCP, API, and FILER interfaces Agents connect over MCP with a context layer and data catalog behind them. | Datafabric | One fabric, every consumer → |
Hot history for agents Years of ground truth stay queryable in seconds, for any consumer. | Datafabric | Hot Search → |
Evidence-attached reasoning Conclusions arrive with the records behind them, auditable end to end. | SynthAI | How SynthAI investigates → |
Starts from any trigger An alert, a ticket, or a question typed by a human or an agent. | SynthAI | One engine, every domain → |
Inside your cloud The substrate your agents stand on stays under your governance. | Datafabric | Deployment → |
From the blog
Reading on agents and telemetry.
Agent memory, grounding, and what AI changes for security and operations.
Why your MDR needs AI
As someone who has been navigating the cybersecurity landscape for quite some time, I’ve seen the evolution of threat detection and response firsthand. From the days of basic antivirus programs to tod
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From Headlines to Slack: Automating Cyber Threat Intelligence Delivery
As a Cybersecurity Analyst, staying ahead of the ever-evolving threat landscape is a non-negotiable part of the job. But in a fast-paced environment, manually looking through multiple sources for the
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The Future of Security Operations: AI and Human Collaboration
As AI continues to evolve, the future of security operations lies in effective collaboration between human analysts and AI systems.
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GTG-1002: AI Orchestrated Cyber Espionage Campaign
In mid-September 2025, Anthropic's Threat Intelligence team detected and disrupted a cyber espionage campaign attributed with high confidence to a Chinese state-sponsored group designated GTG-1002. It
Blog
In depth
Four longer reads on grounded AI.
The Agentic Data Plane: Bloo in the AI Stack
The agentic data plane is the infrastructure layer autonomous agents reason over. Structured, retained telemetry is its foundation.
6 min read
Telemetry Intelligence: Enterprise Infrastructure Layer
Telemetry Intelligence transforms telemetry into long-term, machine-consumable memory. The infrastructure layer after SIEM.
6 min read
Bloo: The System of Record for Enterprise Telemetry
Bloo is the system of record for enterprise telemetry, full-fidelity retention, predictable cost, inside your cloud, built for machines.
5 min read
AI-Native Incident Response Needs Full-Fidelity History
AI-native IR depends on looking back across years of telemetry in seconds. Learn why sampled storage breaks, and what replaces it.
13 min read
Go deeper on the capability
Questions
Agents and grounding.
How do agents connect to Bloo?
MCP is the flagship interface. Agents connect over Model Context Protocol and reason over Datafabric directly, with no glue code. A context layer and a data catalog mean an agent never needs to know what data exists where, or which query language to speak: the fabric generates the query, the context, and the answer.
What does grounding an agent actually change?
An ungrounded agent works from assumptions and returns claims it cannot support. A grounded agent reasons over your retained telemetry, so every output resolves to specific records. The difference is whether your automation asserts or evidences.
Does our telemetry leave our cloud for the agent to use it?
No. Datafabric deploys inside your cloud account, and agents reason over the fabric where it lives. The substrate your agents stand on stays under your governance, retention, and access control.
Do we need a separate store to feed our agents?
No. The agents reason over the same Datafabric your teams and tools already use. There is no second copy to build, sync, or secure, and no export path to maintain.
Give your agents a place to stand.
Ground your AI workflows in a telemetry substrate your team owns, and let every automated conclusion carry its evidence.